├── images ├── 1.JPG ├── 2.JPG ├── Loss.jpeg ├── U-Net-Graph.png └── u-net-architecture.png ├── LICENSE ├── README.md ├── Resize.ipynb ├── CODE_OF_CONDUCT.md └── requirements.txt /images/1.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArkaJU/U-Net-Satellite/HEAD/images/1.JPG -------------------------------------------------------------------------------- /images/2.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArkaJU/U-Net-Satellite/HEAD/images/2.JPG -------------------------------------------------------------------------------- /images/Loss.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArkaJU/U-Net-Satellite/HEAD/images/Loss.jpeg -------------------------------------------------------------------------------- /images/U-Net-Graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArkaJU/U-Net-Satellite/HEAD/images/U-Net-Graph.png -------------------------------------------------------------------------------- /images/u-net-architecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArkaJU/U-Net-Satellite/HEAD/images/u-net-architecture.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Arka 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # U-Net-Satellite; Segmentation based road detection framework using Keras 2 | 3 | The aim of this project is road detection from satellite images using a variant of deep Convolutional Neural Networks which is known as U-Net. The output of the model is a segmentation mask which differentiates roads from other terrains. 4 | 5 | 6 | ## Overview 7 | 8 | ### Model 9 | 10 | U-net is an encoder-decoder type network architecture for image segmentation. The name of the architecture comes from its unique shape, where the feature maps from convolution part in downsampling step are fed to the up-convolution part in up-sampling step. 11 | This model is a slight modification of original U-Net architecture. The details of the architecture can be clearly seen in the notebook as the output of the "model.summary()" cell. 12 | 13 | ![](images/u-net-architecture.png) 14 | 15 | 16 | 17 | 18 | 19 | The TensorBoard graph is shown below: 20 | ![](images/U-Net-Graph.png) 21 | 22 | 23 | 24 | ### Data 25 | 26 | The inputs are resized to 256x256 from the original size of 600x600. The ground truths are binarized version of the input satellite images. Images are separated into train and test sets and stored in separate h5 files for convenience. Training set consists of 2087 images and test set consists of 20 images. 27 | 28 | 29 | ### Training 30 | 31 | The model was trained for 30 epochs and took about 3 hours on colab GPU. After 30 epochs, the training Dice score and Jaccard index were 0.8918 and 0.9754. Corresponding validation metrics were 0.7533 and 0.9527. 32 | The training vs validation loss is shown below : 33 | 34 | 35 | ![](images/Loss.jpeg) 36 | 37 | 38 | 39 | 40 | ## Results 41 | 42 | The following images show the comparison of the original satellite image, ground truth, and it's corresponding prediction: 43 | 44 | ![](images/1.JPG) 45 | ![](images/2.JPG) 46 | 47 | ## References 48 | 49 | - Original paper: [Arxiv](https://arxiv.org/abs/1505.04597) 50 | - Useful blogs: 51 | - [Vehicle Detection](https://chatbotslife.com/small-u-net-for-vehicle-detection-9eec216f9fd6) 52 | - [Medical Image Segmentation](https://towardsdatascience.com/medical-image-segmentation-part-1-unet-convolutional-networks-with-interactive-code-70f0f17f46c6) 53 | - Original Dataset: https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/maps.tar.gz 54 | - Datasets used: https://drive.google.com/drive/folders/1_wBiTCBZ26F_NNpJshw5kwEHyaSeyzFC?usp=sharing 55 | 56 | -------------------------------------------------------------------------------- /Resize.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Code to resize images." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 17, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import cv2\n", 17 | "from PIL import Image\n", 18 | "import numpy as np\n", 19 | "import h5py" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": null, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "#threshold function is disabled when resizing input images\n", 29 | "path = \"masks_train1/\"\n", 30 | "\n", 31 | "for i in range(1,2088):\n", 32 | " dim = (256, 256) #(w,h)\n", 33 | " image = cv2.imread(path + str(i) + \".png\", 0)\n", 34 | " resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)\n", 35 | " (thresh, im_bw) = cv2.threshold(resized, 128, 255, cv2.THRESH_BINARY)\n", 36 | " cv2.imwrite('mask_train/' + str(i) + '.png', im_bw)" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 25, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "images = []\n", 46 | "masks = []\n", 47 | "for i in range(1, 2088):\n", 48 | " img = Image.open(\"images_train/\" + str(i) + \".png\")\n", 49 | " arr = np.array(img)\n", 50 | " images.append(arr)\n", 51 | " img = Image.open(\"mask_train/\" + str(i) + \".png\")\n", 52 | " arr = np.array(img)\n", 53 | " arr = np.expand_dims(arr, -1)\n", 54 | " masks.append(arr)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 27, 60 | "metadata": {}, 61 | "outputs": [ 62 | { 63 | "data": { 64 | "text/plain": [ 65 | "(2087, 256, 256, 1)" 66 | ] 67 | }, 68 | "execution_count": 27, 69 | "metadata": {}, 70 | "output_type": "execute_result" 71 | } 72 | ], 73 | "source": [ 74 | "images = np.array(images)\n", 75 | "masks = np.array(masks)\n", 76 | "masks.shape" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 23, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "with h5py.File(\"Dataset_train.h5\", 'w') as hdf:\n", 86 | " hdf.create_dataset('images', data=images, compression='gzip', compression_opts=9)\n", 87 | " hdf.create_dataset('masks', data=masks, compression='gzip', compression_opts=9)" 88 | ] 89 | } 90 | ], 91 | "metadata": { 92 | "kernelspec": { 93 | "display_name": "Python 3", 94 | "language": "python", 95 | "name": "python3" 96 | }, 97 | "language_info": { 98 | "codemirror_mode": { 99 | "name": "ipython", 100 | "version": 3 101 | }, 102 | "file_extension": ".py", 103 | "mimetype": "text/x-python", 104 | "name": "python", 105 | "nbconvert_exporter": "python", 106 | "pygments_lexer": "ipython3", 107 | "version": "3.6.5" 108 | } 109 | }, 110 | "nbformat": 4, 111 | "nbformat_minor": 2 112 | } 113 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. 6 | 7 | ## Our Standards 8 | 9 | Examples of behavior that contributes to creating a positive environment include: 10 | 11 | * Using welcoming and inclusive language 12 | * Being respectful of differing viewpoints and experiences 13 | * Gracefully accepting constructive criticism 14 | * Focusing on what is best for the community 15 | * Showing empathy towards other community members 16 | 17 | Examples of unacceptable behavior by participants include: 18 | 19 | * The use of sexualized language or imagery and unwelcome sexual attention or advances 20 | * Trolling, insulting/derogatory comments, and personal or political attacks 21 | * Public or private harassment 22 | * Publishing others' private information, such as a physical or electronic address, without explicit permission 23 | * Other conduct which could reasonably be considered inappropriate in a professional setting 24 | 25 | ## Our Responsibilities 26 | 27 | Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. 28 | 29 | Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. 30 | 31 | ## Scope 32 | 33 | This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. 34 | 35 | ## Enforcement 36 | 37 | Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at arkap.saha47@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. 38 | 39 | Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. 40 | 41 | ## Attribution 42 | 43 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version] 44 | 45 | [homepage]: http://contributor-covenant.org 46 | [version]: http://contributor-covenant.org/version/1/4/ 47 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.12.0 2 | alabaster==0.7.12 3 | albumentations==0.1.12 4 | altair==4.1.0 5 | appdirs==1.4.4 6 | argon2-cffi==20.1.0 7 | arviz==0.11.2 8 | astor==0.8.1 9 | astropy==4.2.1 10 | astunparse==1.6.3 11 | async-generator==1.10 12 | atari-py==0.2.9 13 | atomicwrites==1.4.0 14 | attrs==21.2.0 15 | audioread==2.1.9 16 | autograd==1.3 17 | Babel==2.9.1 18 | backcall==0.2.0 19 | beautifulsoup4==4.6.3 20 | bleach==3.3.0 21 | blis==0.4.1 22 | bokeh==2.3.2 23 | Bottleneck==1.3.2 24 | branca==0.4.2 25 | bs4==0.0.1 26 | CacheControl==0.12.6 27 | cached-property==1.5.2 28 | cachetools==4.2.2 29 | catalogue==1.0.0 30 | certifi==2020.12.5 31 | cffi==1.14.5 32 | cftime==1.5.0 33 | chainer==7.4.0 34 | chardet==3.0.4 35 | click==7.1.2 36 | cloudpickle==1.3.0 37 | cmake==3.12.0 38 | cmdstanpy==0.9.5 39 | colorcet==2.0.6 40 | colorlover==0.3.0 41 | community==1.0.0b1 42 | contextlib2==0.5.5 43 | convertdate==2.3.2 44 | coverage==3.7.1 45 | coveralls==0.5 46 | crcmod==1.7 47 | cufflinks==0.17.3 48 | cupy-cuda101==7.4.0 49 | cvxopt==1.2.6 50 | cvxpy==1.0.31 51 | cycler==0.10.0 52 | cymem==2.0.5 53 | Cython==0.29.23 54 | daft==0.0.4 55 | dask==2.12.0 56 | datascience==0.10.6 57 | debugpy==1.0.0 58 | decorator==4.4.2 59 | defusedxml==0.7.1 60 | descartes==1.1.0 61 | dill==0.3.3 62 | distributed==1.25.3 63 | dlib==19.18.0 64 | dm-tree==0.1.6 65 | docopt==0.6.2 66 | docutils==0.17.1 67 | dopamine-rl==1.0.5 68 | earthengine-api==0.1.266 69 | easydict==1.9 70 | ecos==2.0.7.post1 71 | editdistance==0.5.3 72 | en-core-web-sm==2.2.5 73 | entrypoints==0.3 74 | ephem==3.7.7.1 75 | et-xmlfile==1.1.0 76 | fa2==0.3.5 77 | fastai==1.0.61 78 | fastdtw==0.3.4 79 | fastprogress==1.0.0 80 | fastrlock==0.6 81 | fbprophet==0.7.1 82 | feather-format==0.4.1 83 | filelock==3.0.12 84 | firebase-admin==4.4.0 85 | fix-yahoo-finance==0.0.22 86 | Flask==1.1.4 87 | flatbuffers==1.12 88 | folium==0.8.3 89 | future==0.16.0 90 | gast==0.4.0 91 | GDAL==2.2.2 92 | gdown==3.6.4 93 | gensim==3.6.0 94 | geographiclib==1.50 95 | geopy==1.17.0 96 | gin-config==0.4.0 97 | glob2==0.7 98 | google==2.0.3 99 | google-api-core==1.26.3 100 | google-api-python-client==1.12.8 101 | google-auth==1.30.0 102 | google-auth-httplib2==0.0.4 103 | google-auth-oauthlib==0.4.4 104 | google-cloud-bigquery==1.21.0 105 | google-cloud-bigquery-storage==1.1.0 106 | google-cloud-core==1.0.3 107 | google-cloud-datastore==1.8.0 108 | google-cloud-firestore==1.7.0 109 | google-cloud-language==1.2.0 110 | google-cloud-storage==1.18.1 111 | google-cloud-translate==1.5.0 112 | google-colab==1.0.0 113 | google-pasta==0.2.0 114 | google-resumable-media==0.4.1 115 | googleapis-common-protos==1.53.0 116 | googledrivedownloader==0.4 117 | graphviz==0.10.1 118 | greenlet==1.1.0 119 | grpcio==1.34.1 120 | gspread==3.0.1 121 | gspread-dataframe==3.0.8 122 | gym==0.17.3 123 | h5py==3.1.0 124 | HeapDict==1.0.1 125 | hijri-converter==2.1.1 126 | holidays==0.10.5.2 127 | holoviews==1.14.3 128 | html5lib==1.0.1 129 | httpimport==0.5.18 130 | httplib2==0.17.4 131 | httplib2shim==0.0.3 132 | humanize==0.5.1 133 | hyperopt==0.1.2 134 | ideep4py==2.0.0.post3 135 | idna==2.10 136 | imageio==2.4.1 137 | imagesize==1.2.0 138 | imbalanced-learn==0.4.3 139 | imblearn==0.0 140 | imgaug==0.2.9 141 | importlib-metadata==4.0.1 142 | importlib-resources==5.1.3 143 | imutils==0.5.4 144 | inflect==2.1.0 145 | iniconfig==1.1.1 146 | install==1.3.4 147 | intel-openmp==2021.2.0 148 | intervaltree==2.1.0 149 | ipykernel==4.10.1 150 | ipython==5.5.0 151 | ipython-genutils==0.2.0 152 | ipywidgets==7.6.3 153 | itsdangerous==1.1.0 154 | jax==0.2.13 155 | jaxlib==0.1.66+cuda110 156 | jdcal==1.4.1 157 | jedi==0.18.0 158 | jieba==0.42.1 159 | Jinja2==2.11.3 160 | joblib==1.0.1 161 | jpeg4py==0.1.4 162 | jsonschema==2.6.0 163 | jupysql==0.10.7 164 | jupyter==1.0.0 165 | jupyter-client==5.3.5 166 | jupyter-console==5.2.0 167 | jupyter-core==4.7.1 168 | jupyterlab-pygments==0.1.2 169 | jupyterlab-widgets==1.0.0 170 | kaggle==1.5.12 171 | kapre==0.3.5 172 | Keras==2.4.3 173 | keras-nightly==2.5.0.dev2021032900 174 | Keras-Preprocessing==1.1.2 175 | keras-vis==0.4.1 176 | kiwisolver==1.3.1 177 | korean-lunar-calendar==0.2.1 178 | librosa==0.8.0 179 | lightgbm==2.2.3 180 | llvmlite==0.34.0 181 | lmdb==0.99 182 | LunarCalendar==0.0.9 183 | lxml==4.2.6 184 | Markdown==3.3.4 185 | MarkupSafe==2.0.1 186 | matplotlib==3.2.2 187 | matplotlib-inline==0.1.2 188 | matplotlib-venn==0.11.6 189 | missingno==0.4.2 190 | mistune==0.8.4 191 | mizani==0.6.0 192 | mkl==2019.0 193 | mlxtend==0.14.0 194 | more-itertools==8.7.0 195 | moviepy==0.2.3.5 196 | mpmath==1.2.1 197 | msgpack==1.0.2 198 | multiprocess==0.70.11.1 199 | multitasking==0.0.9 200 | murmurhash==1.0.5 201 | music21==5.5.0 202 | natsort==5.5.0 203 | nbclient==0.5.3 204 | nbconvert==5.6.1 205 | nbformat==5.1.3 206 | nest-asyncio==1.5.1 207 | netCDF4==1.5.6 208 | networkx==2.5.1 209 | nibabel==3.0.2 210 | nltk==3.2.5 211 | notebook==5.3.1 212 | numba==0.51.2 213 | numexpr==2.7.3 214 | numpy==1.19.5 215 | nvidia-ml-py3==7.352.0 216 | oauth2client==4.1.3 217 | oauthlib==3.1.0 218 | okgrade==0.4.3 219 | opencv-contrib-python==4.1.2.30 220 | opencv-python==4.1.2.30 221 | openpyxl==2.5.9 222 | opt-einsum==3.3.0 223 | osqp==0.6.2.post0 224 | packaging==20.9 225 | palettable==3.3.0 226 | pandas==1.1.5 227 | pandas-datareader==0.9.0 228 | pandas-gbq==0.13.3 229 | pandas-profiling==1.4.1 230 | pandocfilters==1.4.3 231 | panel==0.11.3 232 | param==1.10.1 233 | parso==0.8.2 234 | pathlib==1.0.1 235 | patsy==0.5.1 236 | pexpect==4.8.0 237 | pickleshare==0.7.5 238 | Pillow==7.1.2 239 | pip-tools==4.5.1 240 | plac==1.1.3 241 | plotly==4.4.1 242 | plotnine==0.6.0 243 | pluggy==0.7.1 244 | pooch==1.3.0 245 | portpicker==1.3.9 246 | prefetch-generator==1.0.1 247 | preshed==3.0.5 248 | prettytable==2.1.0 249 | progressbar2==3.38.0 250 | prometheus-client==0.10.1 251 | promise==2.3 252 | prompt-toolkit==1.0.18 253 | protobuf==3.12.4 254 | psutil==5.4.8 255 | psycopg2==2.7.6.1 256 | ptyprocess==0.7.0 257 | py==1.10.0 258 | pyarrow==3.0.0 259 | pyasn1==0.4.8 260 | pyasn1-modules==0.2.8 261 | pycocotools==2.0.2 262 | pycparser==2.20 263 | pyct==0.4.8 264 | pydata-google-auth==1.2.0 265 | pydot==1.3.0 266 | pydot-ng==2.0.0 267 | pydotplus==2.0.2 268 | PyDrive==1.3.1 269 | pyemd==0.5.1 270 | pyerfa==2.0.0 271 | pyglet==1.5.0 272 | Pygments==2.6.1 273 | pygobject==3.26.1 274 | pymc3==3.11.2 275 | PyMeeus==0.5.11 276 | pymongo==3.11.4 277 | pymystem3==0.2.0 278 | PyOpenGL==3.1.5 279 | pyparsing==2.4.7 280 | pyrsistent==0.17.3 281 | pysndfile==1.3.8 282 | PySocks==1.7.1 283 | pystan==2.19.1.1 284 | pytest==3.6.4 285 | python-apt==0.0.0 286 | python-chess==0.23.11 287 | python-dateutil==2.8.1 288 | python-louvain==0.15 289 | python-slugify==5.0.2 290 | python-utils==2.5.6 291 | pytz==2018.9 292 | pyviz-comms==2.0.1 293 | PyWavelets==1.1.1 294 | PyYAML==3.13 295 | pyzmq==22.0.3 296 | qdldl==0.1.5.post0 297 | qtconsole==5.1.0 298 | QtPy==1.9.0 299 | regex==2019.12.20 300 | requests==2.23.0 301 | requests-oauthlib==1.3.0 302 | resampy==0.2.2 303 | retrying==1.3.3 304 | rpy2==3.4.4 305 | rsa==4.7.2 306 | scikit-image==0.16.2 307 | scikit-learn==0.22.2.post1 308 | scipy==1.4.1 309 | screen-resolution-extra==0.0.0 310 | scs==2.1.3 311 | seaborn==0.11.1 312 | semver==2.13.0 313 | Send2Trash==1.5.0 314 | setuptools-git==1.2 315 | Shapely==1.7.1 316 | simplegeneric==0.8.1 317 | six==1.15.0 318 | sklearn==0.0 319 | sklearn-pandas==1.8.0 320 | smart-open==5.0.0 321 | snowballstemmer==2.1.0 322 | sortedcontainers==2.4.0 323 | SoundFile==0.10.3.post1 324 | spacy==2.2.4 325 | Sphinx==1.8.5 326 | sphinxcontrib-serializinghtml==1.1.4 327 | sphinxcontrib-websupport==1.2.4 328 | SQLAlchemy==1.4.15 329 | sqlparse==0.4.1 330 | srsly==1.0.5 331 | statsmodels==0.10.2 332 | sympy==1.7.1 333 | tables==3.4.4 334 | tabulate==0.8.9 335 | tblib==1.7.0 336 | tensorboard==2.5.0 337 | tensorboard-data-server==0.6.1 338 | tensorboard-plugin-wit==1.8.0 339 | tensorflow==2.5.0 340 | tensorflow-datasets==4.0.1 341 | tensorflow-estimator==2.5.0 342 | tensorflow-gcs-config==2.5.0 343 | tensorflow-hub==0.12.0 344 | tensorflow-metadata==1.0.0 345 | tensorflow-probability==0.12.1 346 | termcolor==1.1.0 347 | terminado==0.10.0 348 | testpath==0.5.0 349 | text-unidecode==1.3 350 | textblob==0.15.3 351 | Theano-PyMC==1.1.2 352 | thinc==7.4.0 353 | tifffile==2021.4.8 354 | toml==0.10.2 355 | toolz==0.11.1 356 | torch==1.8.1+cu101 357 | torchsummary==1.5.1 358 | torchtext==0.9.1 359 | torchvision==0.9.1+cu101 360 | tornado==5.1.1 361 | tqdm==4.41.1 362 | traitlets==5.0.5 363 | tweepy==3.10.0 364 | typeguard==2.7.1 365 | typing-extensions==3.7.4.3 366 | tzlocal==1.5.1 367 | uritemplate==3.0.1 368 | urllib3==1.24.3 369 | vega-datasets==0.9.0 370 | wasabi==0.8.2 371 | wcwidth==0.2.5 372 | webencodings==0.5.1 373 | Werkzeug==1.0.1 374 | widgetsnbextension==3.5.1 375 | wordcloud==1.5.0 376 | wrapt==1.12.1 377 | xarray==0.18.2 378 | xgboost==0.90 379 | xkit==0.0.0 380 | xlrd==1.1.0 381 | xlwt==1.3.0 382 | yellowbrick==0.9.1 383 | zict==2.0.0 384 | zipp==3.4.1 385 | --------------------------------------------------------------------------------